Real-time image detection for edge devices: a peach fruit detection application

Detalhes bibliográficos
Autor(a) principal: Assunção, Eduardo
Data de Publicação: 2022
Outros Autores: Gaspar, Pedro D., Alibabaei, Khadijeh, Simões, M.P., Proença, Hugo, Soares, Vasco N.G.J., Caldeira, J.M.L.P.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.11/8159
Resumo: Within the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some applications, such as robot vision in orchards, require computer vision models to run on edge devices while performing inferences at high speed. In this area, most modern applications use an integrated graphics processing unit (GPU). In this work, we propose the use of a tensor processing unit (TPU) accelerator with a Raspberry Pi target device and the state-of-the-art, lightweight, and hardware-aware MobileDet detector model. Our contribution is the extension of the possibilities of using accelerators (the TPU) for edge devices in precision agriculture. The proposed method was evaluated using a novel dataset of peaches with three cultivars, which will be made available for further studies. The model achieved an average precision (AP) of 88.2% and a performance of 19.84 frames per second (FPS) at an image size of 640 × 480. The results obtained show that the TPU accelerator can be an excellent alternative for processing on the edge in precision agriculture.
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spelling Real-time image detection for edge devices: a peach fruit detection applicationDeep learningEdge deviceObject detectionPrecision agricultureTPU acceleratorWithin the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some applications, such as robot vision in orchards, require computer vision models to run on edge devices while performing inferences at high speed. In this area, most modern applications use an integrated graphics processing unit (GPU). In this work, we propose the use of a tensor processing unit (TPU) accelerator with a Raspberry Pi target device and the state-of-the-art, lightweight, and hardware-aware MobileDet detector model. Our contribution is the extension of the possibilities of using accelerators (the TPU) for edge devices in precision agriculture. The proposed method was evaluated using a novel dataset of peaches with three cultivars, which will be made available for further studies. The model achieved an average precision (AP) of 88.2% and a performance of 19.84 frames per second (FPS) at an image size of 640 × 480. The results obtained show that the TPU accelerator can be an excellent alternative for processing on the edge in precision agriculture.Repositório Científico do Instituto Politécnico de Castelo BrancoAssunção, EduardoGaspar, Pedro D.Alibabaei, KhadijehSimões, M.P.Proença, HugoSoares, Vasco N.G.J.Caldeira, J.M.L.P.2022-11-09T09:20:44Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/8159engASSUNÇÃO, Eduardo [et al.] (2022) - Real-time image detection for edge devices: a peach fruit detection application. Future Internet. 14:11. DOI: https://doi.org/10.3390/fi14110323.10.3390/fi14110323info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-10T01:48:38Zoai:repositorio.ipcb.pt:10400.11/8159Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:38:34.821314Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Real-time image detection for edge devices: a peach fruit detection application
title Real-time image detection for edge devices: a peach fruit detection application
spellingShingle Real-time image detection for edge devices: a peach fruit detection application
Assunção, Eduardo
Deep learning
Edge device
Object detection
Precision agriculture
TPU accelerator
title_short Real-time image detection for edge devices: a peach fruit detection application
title_full Real-time image detection for edge devices: a peach fruit detection application
title_fullStr Real-time image detection for edge devices: a peach fruit detection application
title_full_unstemmed Real-time image detection for edge devices: a peach fruit detection application
title_sort Real-time image detection for edge devices: a peach fruit detection application
author Assunção, Eduardo
author_facet Assunção, Eduardo
Gaspar, Pedro D.
Alibabaei, Khadijeh
Simões, M.P.
Proença, Hugo
Soares, Vasco N.G.J.
Caldeira, J.M.L.P.
author_role author
author2 Gaspar, Pedro D.
Alibabaei, Khadijeh
Simões, M.P.
Proença, Hugo
Soares, Vasco N.G.J.
Caldeira, J.M.L.P.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico de Castelo Branco
dc.contributor.author.fl_str_mv Assunção, Eduardo
Gaspar, Pedro D.
Alibabaei, Khadijeh
Simões, M.P.
Proença, Hugo
Soares, Vasco N.G.J.
Caldeira, J.M.L.P.
dc.subject.por.fl_str_mv Deep learning
Edge device
Object detection
Precision agriculture
TPU accelerator
topic Deep learning
Edge device
Object detection
Precision agriculture
TPU accelerator
description Within the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some applications, such as robot vision in orchards, require computer vision models to run on edge devices while performing inferences at high speed. In this area, most modern applications use an integrated graphics processing unit (GPU). In this work, we propose the use of a tensor processing unit (TPU) accelerator with a Raspberry Pi target device and the state-of-the-art, lightweight, and hardware-aware MobileDet detector model. Our contribution is the extension of the possibilities of using accelerators (the TPU) for edge devices in precision agriculture. The proposed method was evaluated using a novel dataset of peaches with three cultivars, which will be made available for further studies. The model achieved an average precision (AP) of 88.2% and a performance of 19.84 frames per second (FPS) at an image size of 640 × 480. The results obtained show that the TPU accelerator can be an excellent alternative for processing on the edge in precision agriculture.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-09T09:20:44Z
2022
2022-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.11/8159
url http://hdl.handle.net/10400.11/8159
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv ASSUNÇÃO, Eduardo [et al.] (2022) - Real-time image detection for edge devices: a peach fruit detection application. Future Internet. 14:11. DOI: https://doi.org/10.3390/fi14110323.
10.3390/fi14110323
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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